Explore global development with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

We see an interesting spread with an outlier to the right. Answer the following questions, please:

  1. Why does it make sense to have a log10 scale on x axis?

Using log-transform allow us to fit a linear regression rather than a polynomial to the data. Also it deals with outliers, as illustrated below, including the outlier, but NOT log-transforming, would make it difficult to get a sense of the data visually.

gapminder %>% 
  filter(year==1952) %>% 
  ggplot(aes(gdpPercap, lifeExp, size = pop))+
  geom_point()

  1. Who is the outlier (the richest country in 1952 - far right on x axis)?
gapminder %>%
  filter(year == 1952) %>% 
  slice(which.max(gdpPercap))
## # A tibble: 1 × 6
##   country continent  year lifeExp    pop gdpPercap
##   <fct>   <fct>     <int>   <dbl>  <int>     <dbl>
## 1 Kuwait  Asia       1952    55.6 160000   108382.

From executing the chunk above we see that Kuwait is the outlier with a GDP in 1952 of 108.382.

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Tasks:

  1. Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)
options("scipen" = 100) # removing scientific notation 

gapminder %>%
  filter(year == 2007) %>% 
   ggplot(aes(gdpPercap, lifeExp, size=pop, color=continent))+
    geom_point()+
    scale_x_log10()+
    ggtitle("Life Expectancy and GDP in 2007", subtitle = "With point size determined by population size and color by continent")+
    xlab("GDP per Capita")+
    ylab("Life Expectancy (in years)")+
    labs(color="Continent", size="Population size")+
    theme(
      plot.title = element_text(hjust = 0.5, size = 18),
      plot.subtitle = element_text(hjust = 0.5, size = 11, face="italic"))

  1. What are the five richest countries in the world in 2007?
gapminder %>%
  filter(year == 2007) %>% 
  slice_max(gdpPercap, n=5) %>% 
  select(country, gdpPercap)
## # A tibble: 5 × 2
##   country       gdpPercap
##   <fct>             <dbl>
## 1 Norway           49357.
## 2 Kuwait           47307.
## 3 Singapore        47143.
## 4 United States    42952.
## 5 Ireland          40676.

From executing the chunk above we see that Norway, Kuwait, Singapore, The United States and Ireland are the top 5 richest countries in 2007.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

  1. Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

  2. Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.

gapminder %>%
  ggplot(aes(gdpPercap, lifeExp, size=pop, color=continent))+
  geom_point()+ 
  scale_x_log10()+
  transition_time(year)+
  labs(title = 'Development of Life Expectancy and GDP',
       subtitle = "Year: {frame_time}",
       x = "GDP per capita", 
       y = "Life Expectancy", 
       color="Continent", 
       size="Population size")

  1. Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

Question:

I want to see whether there’s a correlation between CO2-emissions and the wealth of a country (as measured by GDP). I do this by downloading a dataset containing CO2 emission from the gapminder website and combining with the gapminder data we’ve been working with so far.

# loading a csv showing CO2 emissions pr person
df_emission <- read_csv("co2_emissions_tonnes_per_person.csv")
## Rows: 194 Columns: 220
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (5): country, 1830, 1831, 1832, 1833
## dbl (215): 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
df_emission_2007 <- df_emission %>% 
  select(country, `2018`) %>% 
  rename(Emission=`2018`)

gapminder_2007 <- gapminder %>% 
  filter(year==2007)

df_emission_2007_full <- merge(df_emission_2007, gapminder_2007)
head(df_emission_2007_full)
##       country Emission continent year lifeExp      pop  gdpPercap
## 1 Afghanistan    0.254      Asia 2007  43.828 31889923   974.5803
## 2     Albania    1.590    Europe 2007  76.423  3600523  5937.0295
## 3     Algeria    3.690    Africa 2007  72.301 33333216  6223.3675
## 4      Angola    1.120    Africa 2007  42.731 12420476  4797.2313
## 5   Argentina    4.410  Americas 2007  75.320 40301927 12779.3796
## 6   Australia   16.900   Oceania 2007  81.235 20434176 34435.3674
df_emission_2007_full %>% 
  ggplot(aes(Emission, gdpPercap, color=continent))+
  geom_point()+
  scale_x_log10()+
  labs(
    title="CO2 Emission and GDP in 2007",
    x="CO2 emission pr. person (log scale)",
    y="GDP per capita",
    color="Continent")+
  theme(plot.title = element_text(hjust = 0.5, size = 18))

Answer:

The graph does indeed seem to suggest that our general CO2 emission pr. person increases as the wealth of a country grows. The graph also suggests that generally European countries omit more CO2 pr. person than people from African countries, and interestingly enough also seem to omit more CO2 than people in American countries.